Menninger Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA.
Electrical and Computer Engineering, Rice University, Houston, TX, USA.
Behav Neurol. 2023 Aug 5;2023:8552180. doi: 10.1155/2023/8552180. eCollection 2023.
Suicide is one of the leading causes of death across different age groups. The persistence of suicidal ideation and the progression of suicidal ideations to action could be related to impulsivity, the tendency to act on urges with low temporal latency, and little forethought. Quantifying impulsivity could thus help suicidality estimation and risk assessments in ideation-to-action suicidality frameworks.
To model suicidality with impulsivity quantification, we obtained questionnaires, behavioral tests, heart rate variability (HRV), and resting state functional magnetic resonance imaging measurements from 34 participants with mood disorders. The participants were categorized into three suicidality groups based on their Mini-International Neuropsychiatric Interview: none, low, and moderate to severe.
Questionnaire and HRV-based impulsivity measures were significantly different between the suicidality groups with higher subscales of impulsivity associated with higher suicidality. A multimodal system to characterize impulsivity objectively resulted in a classification accuracy of 96.77% in the three-class suicidality group prediction task.
This study elucidates the relative sensitivity of various impulsivity measures in differentiating participants with suicidality and demonstrates suicidality prediction with high accuracy using a multimodal objective impulsivity characterization in participants with mood disorders.
自杀是不同年龄群体的主要死亡原因之一。自杀意念的持续存在以及自杀意念向行动的发展可能与冲动有关，即倾向于在时间延迟低且事先思考较少的情况下对冲动做出反应。因此，量化冲动性可能有助于在从意念到行动的自杀意念框架中评估自杀意念和风险。
为了用冲动性量化来构建自杀模型，我们从 34 名患有心境障碍的参与者那里获得了问卷、行为测试、心率变异性 (HRV) 和静息状态功能磁共振成像测量结果。根据他们的《迷你国际神经精神访谈》，参与者被分为三组自杀意念：无、低和中到重度。
问卷和基于 HRV 的冲动性测量在自杀意念组之间存在显著差异，较高的冲动性亚量表与较高的自杀意念相关。一个多模态系统客观地描述冲动性，在区分有自杀意念的参与者时，其分类准确率达到 96.77%。
这项研究阐明了各种冲动性测量在区分有自杀意念的参与者方面的相对敏感性，并在患有心境障碍的参与者中使用多模态客观冲动性特征来实现高准确率的自杀预测。
